Sensor power management

ABSTRACT

Embodiments of the present disclosure provide techniques and configurations for an apparatus to reduce sensor power consumption, in particular, through predictive data measurements by one or more sensors. In one instance, the apparatus may include one or more sensors and a sensor management module coupled with the sensors and configured to cause the sensors to initiate measurements of data indicative of a process in a first data measurement mode, determine a pattern of events comprising the process based on a portion of the measurements collected by the sensors in the first data measurement mode over a time period, and initiate measurements of the data by the one or more sensors in a second data measurement mode. The second data measurement mode may be based on the pattern of events comprising the process. The pattern may indicate a prediction of appearance of events in the process. Other embodiments may be described and/or claimed.

FIELD

Embodiments of the present disclosure generally relate to the field ofsensor devices, and more particularly, to managing sensing devices tosave power.

BACKGROUND

There is a variety of different types of sensors, includingaccelerometers, gyroscopes, barometers, infrared proximity sensors,visible light sensors, microphones, compasses, thermometers, moisturesensors, etc. Such sensors typically capture raw data, which may then beinterpreted. Different sensor types consume different amounts of power.Power management for sensors may include different methods. However,currently employed methods of sensor power management may not always besufficient to reduce sensor power consumption to a desired level.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments will be readily understood by the following detaileddescription in conjunction with the accompanying drawings. To facilitatethis description, like reference numerals designate like structuralelements. Embodiments are illustrated by way of example and not by wayof limitation in the figures of the accompanying drawings.

FIG. 1 schematically illustrates an example system for managing datameasurements by one or more sensors in accordance with some embodimentsof the present disclosure.

FIG. 2 is a schematic representation of an example predictive datameasurement performance technique, using one or more sensors for datameasurements of a process, according to some embodiments.

FIG. 3 is a schematic representation of another example predictive datameasurement performance technique, using one or more sensors for datameasurements of a process, according to some embodiments.

FIG. 4 is a schematic representation of another example predictive datameasurement performance technique, using one or more sensors for datameasurements of a process, according to some embodiments.

FIG. 5 is a process flow diagram for performing predictive datameasurements by one or more sensors, in accordance with someembodiments.

FIG. 6 is another process flow diagram for performing data measurementsby one or more sensors, in accordance with some embodiments.

FIG. 7 is a block diagram illustrating different examples of performingdata measurements in the second data measurement mode described inreference to FIGS. 2-6, according to some embodiments.

FIG. 8 illustrates an example computing device suitable for use withvarious components of FIG. 1, such as a computing device configured tooperate the sensor module, in accordance with various embodiments.

DETAILED DESCRIPTION

Embodiments of the present disclosure include techniques andconfigurations to reduce sensor power consumption, in particular,through predictive data measurements by one or more sensors. Inaccordance with embodiments, an apparatus may include one or moresensors and sensor management modules coupled with the sensors andconfigured to cause the sensors to initiate measurements of dataindicative of a process in a first data measurement mode, determine apattern of events comprising the process based on a portion of themeasurements collected by the sensors in the first data measurement modeover a time period, and initiate measurements of the data by the one ormore sensors in a second data measurement mode. The second mode may bebased on the pattern of events comprising the process. The pattern mayindicate a prediction of appearance of events in the process. The firstdata measurement mode may include performing data measurements by thesensors at a first (e.g., fixed or variable) sampling rate.

The second data measurement mode may include performing the datameasurements at a second sampling rate that is lower than the firstsampling rate, performing the data measurements at a third sampling rateduring periods of time corresponding to predicted appearances of eventsaccording to the determined pattern, or performing the data measurementsat a fourth sampling rate during periods of time between predictedappearances of consecutive events in the process and performing the datameasurements at a fifth sampling rate during the periods of timecorresponding to predicted appearances of events. The fifth samplingrate may be greater than the fourth sampling rate. In some embodiments,the fifth sampling rate may be slower than the fourth sampling rate.

In the following detailed description, reference is made to theaccompanying drawings that form a part hereof, wherein like numeralsdesignate like parts throughout, and in which are shown by way ofillustration embodiments in which the subject matter of the presentdisclosure may be practiced. It is to be understood that otherembodiments may be utilized and structural or logical changes may bemade without departing from the scope of the present disclosure.Therefore, the following detailed description is not to be taken in alimiting sense, and the scope of embodiments is defined by the appendedclaims and their equivalents.

For the purposes of the present disclosure, the phrase “A and/or B”means (A), (B), or (A and B). For the purposes of the presentdisclosure, the phrase “A, B, and/or C” means (A), (B), (C), (A and B),(A and C), (B and C), or (A, B, and C).

The description may use perspective-based descriptions such astop/bottom, in/out, over/under, and the like. Such descriptions aremerely used to facilitate the discussion and are not intended torestrict the application of embodiments described herein to anyparticular orientation.

The description may use the phrases “in an embodiment,” or “inembodiments,” which may each refer to one or more of the same ordifferent embodiments. Furthermore, the terms “comprising,” “including,”“having,” and the like, as used with respect to embodiments of thepresent disclosure, are synonymous.

The term “coupled with,” along with its derivatives, may be used herein.“Coupled” may mean one or more of the following. “Coupled” may mean thattwo or more elements are in direct physical, electrical, or opticalcontact. However, “coupled” may also mean that two or more elementsindirectly contact each other, but yet still cooperate or interact witheach other, and may mean that one or more other elements are coupled orconnected between the elements that are said to be coupled with eachother.

FIG. 1 schematically illustrates an example system 100 for managing datameasurements by one or more sensors in accordance with some embodimentsof the present disclosure. The system 100 may be used in variousimplementations. For example, the system 100 may comprise a wearabledevice configured to provide sensor measurement of data related to auser's activities. In another example, the system 100 may comprise asensing device control system configured to manage multiple sensors tomeasure data related to a particular physical, chemical, biological,biometric, or other process.

In some embodiments, the system 100 may comprise a computing device 102configured to manage a sensor module 101 that may include one or moresensors 106, 108, 110 (in some embodiments, a sensor array). The sensormodule 101 may be communicatively coupled 170 with the computing device102. The computing device 102 may include a processor 132 configured tomanage the sensors 106, 108, 110 and to process data measured by thesensors and provided to the computing device 102. More specifically, thecomputing device 102 may include memory 134 having instructions (e.g.,compiled in a sensor management module 150) that, when executed on theprocessor 132, may cause the processor 132 to perform sensor managementoperations adapted to save power consumed by the sensors 106, 108, 110during the data measurement process.

For example, the sensor management module 150 may be configured tocontrol power supply to the sensors 106, 108, 110 provided by a powersupply module 130. The power supply module 130 may include a powersource coupled 160, 162 with the computing device 102 and sensors 106,108, 110 and configured to supply power to at least some (or all) of thesensors 106, 108, 110. The sensor management module 150 may managepowering on and off the sensors 106, 108, 110 by the power supply module130.

In some embodiments, at least some of the sensors 106, 108, 110 mayinclude a power supply source (e.g., built in or operatively coupledwith the sensor(s)), such as a battery, which may be controlled (e.g.,turned on and off) by the sensor management module 150.

Memory 134 may further include instructions (e.g., compiled in a dataprocessing module 146) that, when executed on the processor 132, maycause the processor 132 to perform data processing of the data measuredby the sensors 106, 108, 110.

The computing device 102 may include other components 144 necessary forthe functioning of the computing device 102. For example, the computingdevice 102 may include a display (not shown) configured to display atleast the results of the data measurements by the sensors 106, 108, 110,which may be provided by the execution of the module 146. The processor132, memory 134, and other components 144 of the computing device 102may be coupled with one or more interfaces (not shown) configured tofacilitate information exchange among the above-mentioned components.Communications interface(s) (not shown) may provide an interface for thecomputing device 102 to communicate over one or more wired or wirelessnetwork(s) and/or with any other suitable device. An exampleconfiguration of the system 100 including the computing device 102 isdescribed in greater detail in reference to FIG. 8.

As discussed, the sensor management module 150 and data processingmodule 146 may be implemented as a software component stored, e.g., inthe memory 134 and configured to execute on the processor 132. In someembodiments, the sensor management module 150 and data processing module146 may be implemented as a combination of software and hardwarecomponents. In some embodiments, the sensor management module 150 anddata processing module 146 may include a hardware implementation.

As described above, the sensor module 101 may include sensors (e.g.,sensor array) 106, 108, 110 operatively coupled 120 with a process 180and configured to measure data indicative of the process 180. Thesensors 106, 108, 110 are shown in FIG. 1 for illustrative purposesonly; it will be appreciated that any number of sensors (e.g., one ormore) may be used in the sensor module 101.

Generally, the process 180 may be any type of digital or analog process,continuous or periodic, that may be defined in measurable physicalquantities, which may be convertible into a signal readable by arespective sensing device. Examples of measurable processes may include,but are not limited to, light, various types of motion, temperature,magnetic fields, gravity, humidity, moisture, vibration, pressure,electrical fields, sound, and other physical aspects of the external orinternal environment. Other examples may include chemical composition ofa matter or environment, biometric processes, and the like. Themeasurable data indicative of a process may include different physicalcharacteristics and parameters. Accordingly, sensors 106, 108, 110 mayinclude different types of sensors, including, but not limited to,accelerometers, gyroscopes, barometers, infrared proximity sensors,visible light sensors, transducers, actuators, and the like.

The process 180 may comprise events 182, 184, which may be indicative ofthe process 180. For example, a user's heartbeats may be consideredevents of the user's heart activity and may be measurable by arespective sensing device (e.g., heart rate monitor). The events 182,184 comprising the process 180 may be of particular interest because theprocess 180 may be defined or otherwise understood by interpreting theevents comprising the process and measured by the sensors 106, 108, 110.Referring to FIG. 1, the events 182, 184 represented as peaks of a graph190 depicting the process 180 may be indicative of the process 180.Accordingly, the sensors 106, 108, 110 may be configured to performmeasurements of the graph 190 values, including values representingevents 182, 184 to obtain indications of the process 180, in accordancewith embodiments described herein. (While only two events 182, 184 areillustrated in the process 180 for simplicity purposes, it will beunderstood that the process 180 may comprise multiple events similar to182, 184.)

At least some measurable processes (e.g., process 180) may includeevents (e.g., events 182, 184) that may occur at substantially regularintervals. For example, a user's heartbeats or walking (running) stepsmay occur at regular intervals, e.g., with substantial periodicity.Accordingly, a pattern associated with such a process may be determinedthat may indicate a prediction of occurrence (appearance) of events inthe process. The pattern associated with a process may be identified(e.g., derived from data indicative of the process) using a number ofprocess data measurements accumulated over a period of time. After thepattern is identified, e.g., appearance of process events occurring atsubstantially regular intervals is predicted from the collected data,the pattern (e.g., appearance of process events as predicted) may becontinuously verified, using the techniques described herein.

FIG. 2 is a schematic representation 200 of an example predictive datameasurement performance technique, using one or more sensors for datameasurements of a process, according to some embodiments. The processmay be implemented using, for example, components described in referenceto FIG. 1, for example, the sensor management module 150 and dataprocessing module 146, and sensors 106, 108, 110.

The representation 200 may include a process 202 to be measured by theone or more sensors. As shown, the process 202 may comprise multipleevents 204, 206, 208 . . . that may occur with substantial regularity(e.g., at substantially regular time intervals). The X axis of theprocess 202 may indicate duration (e.g., time T) or frequency of eventsappearance (e.g., F) of the process 202, while the Y axis may indicateamplitude value A associated with the process 202. The process datameasurements 210 using the sensors 106, 108, 110 are schematicallydepicted by multiple vertical lines 212 placed along the X axis withdetermined intervals (hereinafter “rate”). Each line 212 may indicate aninstance of measurement of the process 202 performed by at least one ofthe sensors 106, 108, or 110. As briefly discussed above, a conventionalmeasurement technique that may be applied to the process 202 may includeperforming the data measurements at a fixed sampling rate, e.g., atfixed intervals or time periods.

In contrast, the measurements 210 illustrated in FIG. 2 may occur usingat least two data measurement modes. For example, the measurements 210of data indicative of the process 202 may be initiated by the one ormore sensors in a first data measurement mode 214. The first datameasurement mode 214 may comprise causing, by the sensor managementmodule 150, the sensors 106, 108, 110 to perform the data measurementsat a first sampling (e.g., fixed) rate R₁, for example, every fewseconds. It will be understood that causing the sensors 106, 108, 110 toperform the data measurements may commence by causing, by the sensormanagement module 150, the sensors 106, 108, 110 to power on.

The sensor management module 150 and/or data processing module 146 maydetermine a pattern of events comprising the process 202, based at leaston a portion of the measurements collected by the one or more sensors106, 108, 110 in the first data measurement mode 214 over a time periodT₁ as shown in FIG. 2. For example, determination of a pattern of eventscomprising the process 202 may include identifying data points A₁ (e.g.,corresponding to events 204, 206, 208, and so on) and time instances,e.g., t₁, t₂, and so on (or corresponding time intervals or frequencies)approximately at which the identified data points are to be measured bythe one or more sensors 106, 108, 110. Generally, a pattern may indicatea prediction of appearance of data points indicative of a process, atparticular time intervals, with the desired margin of error.

After the pattern (e.g., a prediction of appearance of events 204, 206,208 in the process 202) is determined, the sensor management module 150may initiate measurements of the data of the process 202 by the one ormore sensors 106, 108, 110 in a second data measurement mode 216, basedon the determined pattern of events 204, 206, 208 comprising the process202. More specifically, the sensor management module 150 may cause thesensors 106, 108, 110 to perform the data measurements at a second(e.g., fixed or continuously or otherwise varied) sampling rate R₂during periods of time T₂ corresponding to predicted appearances ofevents 204, 206, 208 according to the determined pattern, e.g., at timeinstances t₁, t₂, etc. (It will be understood that the time instances ofappearance of events 204, 206, 208 may occur within the periods of timeT₂.)

If the data points corresponding to the predicted appearance of events204, 206, 208, when measured, match the identified data points within adesired margin of error, a conclusion may be made that the pattern ofevents, as defined, correctly (e.g., within the margin of error)describes the process 202. Accordingly, the second data measurement mode216 may comprise periodic verification of the pattern of events 204,206, 208, as determined during the first data measurement mode 214.

Conversely, in the second data measurement mode 216, the sensormanagement module 150 may cause the sensors 106, 108, 110 to refrainfrom performing the data measurements during periods of time T₃corresponding to intervals between the predicted appearances of events204, 206, 208 according to the determined pattern. In some embodiments,the first sampling rate R₁ corresponding to the first data measurementmode 214 may not be the same as the second sampling rate R₂corresponding to the second data measurement mode 216.

During the data measurements in the second data measurement mode 216,the sensor management module 150 and/or the data processing module 146may determine, from the data being measured in the second mode, that themeasured data points corresponding to the events 204, 206, 208 no longermatch the determined pattern. For example, the values of the measureddata points may no longer match the values of the identified data pointsA₁ within a desired margin of error (e.g., first margin). In anotherexample, the times or frequencies of the appearance of the events may nolonger match the predicted events' appearance times or frequencieswithin a desired margin of error (e.g., second margin).

In general, a result of matching the measured events to the patternduring the second data measurement mode 216 may exceed a desired marginof error. Accordingly, it may be determined that the pattern of eventsidentified during the first data measurement mode 214 is no longerverifiable, e.g., it no longer correctly predicts the appearance ofevents 204, 206, 208 comprising the process 202. Based on thisdetermination, the sensor management module 150 may revert to the firstdata measurement mode 214 of data measurements, in order to determine anew pattern, if any. If no new pattern is identified, e.g., after aperiod of time T₁, the sensors 106, 108, 110 may continue to performdata measurements in the first data measurement mode 214.

It will be understood that the total number of measurements 210 in thefirst and second data measurement modes 214 and 216 may be less than anumber of measurements that may occur when performing the datameasurements at a fixed sampling rate, during the same time period.Accordingly, the power consumption by the sensors 106, 108, 110 whenutilizing the techniques described above may be reduced relative to thepower consumption by the sensors 106, 108, 110 when performing the datameasurements at the fixed sampling rate. The amount of saved power maydepend on the rates of data measurements in the first and second datameasurement modes 214 and 216.

It is important to note that the sample rate (e.g., used in the seconddata measurement mode 216) may be fixed (e.g., regular) or varied (e.g.,irregular). For example, the sample rate may comprise taking 20 samplesin 1 second and having the spacing between the samples variable. E.g.,two consecutive sampling rates may be 1/100 seconds apart, then 1/10seconds apart, and so on.

In another example, sampling rate may vary, e.g., in a continuous oraperiodic manner. For example, a sampling rate may vary over time assome sort of function. For example, the sampling rate may increase(e.g., have the highest value) around a point of interest correspondingto data points A₁ of predicted appearances of events 204, 206, 208, anddecrease gradually toward halfway between points of interest. In otherwords, leading up to a point of interest, the sampling rate may getfaster (higher). Moving away from a point of interest, the sampling ratemay get slower (lower).

FIG. 3 is a schematic representation 300 of another example predictivedata measurement performance technique, using one or more sensors fordata measurements of a process, according to some embodiments. Similarlyto the technique described in reference to FIG. 2, data measurements ofa process 302 may be initially performed in a first data measurementmode 314, similar to 214 described above. Namely, the data measurementsmay be performed at a first (fixed) sampling rate (e.g., R₁ or otherrate value). Once the pattern associated with the process 302 isidentified, the data measurements may be performed in a secondmeasurement mode 316, which may comprise performing data measurements ata second sampling rate (e.g., R₃). The second sampling rate R₃ may belower than the first sampling rate R₁, as shown in FIG. 3, and may bedefined as the lowest possible rate, using which rate the predictedappearance of events 306, 308 of the process 302 may be verified.

FIG. 4 is a schematic representation 400 of another example predictivedata measurement performance technique, using one or more sensors fordata measurements of a process, according to some embodiments. Similarlyto the techniques described in reference to FIGS. 2 and 3, datameasurements of a process 402 may be initially performed in a first datameasurement mode 414, similar to 214 and/or 314 described above. Namely,the data measurements may be performed at a first (fixed) sampling rate(e.g., R₁ or other rate value).

Once the pattern associated with the process 402 is identified, the datameasurements may be performed in a second measurement mode 416, whichmay comprise sub-modes 420 and 422, as shown in FIG. 4. In sub-mode 420,the data measurements may be performed at a second sampling rate R₄during periods of time between predicted appearances of consecutiveevents (e.g., 404, 406, and so on) in the process 402. In the sub-mode422, the data measurements may be performed at a third sampling rate R₅during the periods of time corresponding to appearances of events (e.g.,404, 406, and so on). In some embodiments, the third sampling rate R₅may be greater than the second sampling rate R₄. In other embodiments,the third sampling rate R₅ may be lower than the second sampling rateR₄. For example, the periods of time corresponding to appearances ofevents may be shorter than the periods of time between the appearancesof events, and vice versa.

In addition to the techniques described in reference to FIGS. 2-4, otherdata measurement techniques may be contemplated. For example, a datameasurement technique may be directed at reducing power consumption bythe processor of a computing device engaged in processing measured data,such as the processor 132 of the computing device 102 of FIG. 1. In thisexample, the data measurements may be performed by one or more sensorsin a first data measurement mode at a first sampling rate, the patternof events may be determined, and a second data measurement mode may beinitiated. The second data measurement mode may include, in thisinstance, continuing to perform data measurements by one or more sensorsin the first data measurement mode (e.g., at the first sampling rate),and processing, by the processor, the data measurements that are takenduring periods of time corresponding to the predicted appearance ofevents in the process, while refraining from processing the datameasurements taken at other times.

In some embodiments, data measurements may be performed by multiplesensors. An example data measurement process directed at reducing powerconsumption by the multiple sensors may include performing the datameasurements at a first sampling rate by the multiple sensors (e.g., ina first data measurement mode), determining the pattern of events, andswitching to a second data measurement mode comprising performing thedata measurements at the same first sampling rate by at least one of themultiple sensors and ceasing to perform the data measurements by atleast another one of the multiple sensors. For example, just one sensormay perform data measurements in the second data measurement mode.

FIG. 5 is a process flow diagram 500 for performing predictive datameasurements by one or more sensors, in accordance with someembodiments. The process 500 may be performed, for example, by thesensor management module 150 and data processing module 146 of FIG. 1.

The process 500 may begin at block 502, wherein measurements of dataindicative of a process (e.g., 202, 302, or 402) may be initiated in afirst data measurement mode (e.g., 214, 314, or 414). To initiate themeasurements, the sensor (or sensors) performing the measurements may,e.g., be powered on or reset.

At block 504, a pattern of events comprising the process may bedetermined, based on the measurements collected in the first datameasurement mode over a time period.

At block 506, data measurements may be initiated in a second datameasurement mode (e.g., 216, 316, 416, or others described above), basedon the determined pattern of events comprising the process.

FIG. 6 is another process flow diagram 600 for performing datameasurements by one or more sensors, in accordance with someembodiments. The process 600 elaborates on the process 500 in greaterdetail. The process 600 may be performed, for example, by the sensormanagement module 150 and data processing module 146 of the system 100of FIG. 1. By way of example only, one may assume that the system 100comprises a wearable device, such as a smart watch (e.g., Basis® smartwatch) having a heart rate monitor.

The process 600 may begin at block 602, where data measurements of aprocess may be performed by one or more sensors in a first datameasurement mode (e.g., at a fixed sampling rate), similar to theprocesses described in reference to FIGS. 2-4. Continuing with the smartwatch example, a heart rate monitor may be turned on (powered on) toconduct the user's heart rate measurements in the first data measurementmode.

The data measurements may be scheduled to continue for a determinedperiod of time (measurement cycle). For example, the smart watch heartrate monitor may be set to perform heart rate measurements for aparticular period of time. Accordingly, at decision block 604, it may bedetermined whether the data measurement cycle is over. If the datameasurement cycle is continuing, the process 600 may move to decisionblock 606.

At decision block 606, it may be determined whether the datameasurements of the process have been accumulated since the beginning ofthe data measurement cycle. For example, sufficient sampling data mayneed to be collected in order to perform pattern determination describedin reference to FIG. 2. If sufficient sampling data has not beenaccumulated, the process 600 may revert to block 602. Otherwise, theprocess 600 may move to decision block 608.

At decision block 608, it may be determined whether any eventsindicative of the process have been identified. Continuing with thesmart watch example, the events may comprise heartbeats of the user'sheart. However, the user may not necessarily wear the watch all thetime. For example, if the watch is sitting on the user's desk, the heartmonitor may be powered on, but no heartbeats may be sensed andidentified as events. Accordingly, if no events have been identified,the process 600 may revert to block 602. Otherwise, the process 600 maymove to decision block 610.

At decision block 610, it may be determined whether the pattern ofevents indicative of the process has been identified. Continuing withthe smart watch example, if the watch is worn by the user, the heartrate may be determined over a certain period of time (e.g., one minute).More specifically, the heartbeats of particular amplitude and intervalsbetween them may be identified.

Accordingly, if no events have been identified, the process 600 mayrevert to block 602. Otherwise, the process 600 may move to block(routine) 612, which provides for performing data measurements in asecond data measurement mode. The second data measurement mode mayinclude a number of different data measurement processes, some of whichwere described in reference to FIGS. 2-4. The routine 612 will bedescribed in reference to FIG. 7 in greater detail.

During performance of data measurements in the second data measurementmode, it may be determined, at decision block 614, whether the patternidentified at decision block 612 has changed. More specifically, themeasured events of the process may no longer match the identifiedpattern of events, which may be established during the data measurementperformance in the second data measurement mode as described inreference to FIG. 2. Continuing with the smart watch example, the heartrate determined during the first data measurement mode may pertain tothe user sleeping state. If the user woke up and, for example, startedrunning, her heart rate may change (e.g., increase beyond a particularthreshold) such that the sleeping state heart rate-related pattern mayno longer be correct.

Accordingly, if the pattern has changed, the process 600 may revert toblock 602, to resume performing the data measurements in the first datameasurement mode in order to identify a new pattern of events, if any(e.g., determine new heart rate pattern related to the user's runningstate, following the smart watch example). If the pattern has notchanged, the process 600 may move to decision block 616, where the checkfor the end of the measurement cycle may be performed. If themeasurement cycle has ended, the process 600 ends. Otherwise, the datameasurements continue in the second data measurement mode, as indicatedby block 612.

FIG. 7 is a block diagram 700 illustrating different examples ofperforming data measurements in the second data measurement mode,according to some embodiments. The block diagram 700 provides at leastsome implementations of routine 612 described in reference to FIG. 6.

As shown, routine 612 “Perform data measurements in the second datameasurement mode” may include, but is not limited to, at least one ofthe processes indicated by blocks 702, 704, 706, 708, or 710.

Block 702 may include the process described in reference to FIG. 2.Namely, the process of block 702 may include performing datameasurements during periods of time corresponding to the appearance ofevents in the process, as determined by the pattern of events identifiedduring performance of data measurements in the first data measurementmode.

Block 704 may include the process described in reference to FIG. 3.Namely, the process of block 704 may include performing datameasurements at a sampling rate that is lower than the rate associatedwith the first data measurement mode.

Block 706 may include the process described in reference to FIG. 4.Namely, the process of block 706 may include performing datameasurements at a sampling rate that is lower than the first datameasurement mode rate between appearances of events, and at a different(e.g., higher) rate during periods of time corresponding to appearancesof the events.

Block 708 may include the process configured to reduce power consumptionin the case of multiple sensors performing data measurements. Namely,the number of sensors performing the data measurements at least in thefirst data measurement mode may be reduced. For example, in the seconddata measurement mode, one sensor may perform data measurements (e.g.,continue to perform measurements at a rate corresponding to the firstdata measurement mode) and other sensors may be turned off or switch toanother measurement mode, similar to the second measurement modedescribed in reference to blocks 702, 704, or 706.

In general, at least one sensor may perform measurements at the firstmode rate, and at least one other sensor may be turned off (powered off)or switch to a different measurement mode. For example, in someembodiments, one sensor may be run in the first data measurement mode,while other sensor(s) may be run in the second measurement mode. Forexample, an accelerometer and optical sensor may both be necessary todetermine heart rate. Once the heart rate pattern is determined, theaccelerometer may run continuously in the first measurement mode whilethe optical sensor may run in a second measurement mode.

Block 710 may include the process configured to reduce power consumptionby the processor 132 of the computing device 102 described in referenceto FIG. 1. For example, data measurements may continue to be performedat a sampling rate corresponding to the first data measurement mode. Thedata measured only during periods of time corresponding to theappearance of events in the process may be processed by the processor132, e.g., using the data processing module 146, thus reducing the totalamount of data processed by the processor 132.

In another embodiment, some portion of the sensor data may be bufferedin memory 134. If the pattern is detected to have changed, the processor132 may retrospectively reprocess the buffered data rather than waitingfor new sensor samples to be collected. This may avoid having to waitfor more sensor data to come in if the pattern changes.

FIG. 8 illustrates an example computing device 800 suitable for use withvarious components of FIG. 1, such as computing device 102 configured tooperate the sensor module 101 of FIG. 1, in accordance with variousembodiments. As shown, computing device 800 may include one or moreprocessors or processor cores 802 and system memory 804. For the purposeof this application, including the claims, the terms “processor” and“processor cores” may be considered synonymous, unless the contextclearly requires otherwise. The processor 802 may include any type ofprocessors, such as a central processing unit (CPU), a microprocessor,and the like. The processor 802 may be implemented as an integratedcircuit having multi-cores, e.g., a multi-core microprocessor. Thecomputing device 800 may include mass storage devices 806 (such asdiskette, hard drive, volatile memory (e.g., dynamic random-accessmemory (DRAM), compact disc read-only memory (CD-ROM), digital versatiledisk (DVD), and so forth). In general, system memory 804 and/or massstorage devices 806 may be temporal and/or persistent storage of anytype, including, but not limited to, volatile and non-volatile memory,optical, magnetic, and/or solid state mass storage, and so forth.Volatile memory may include, but is not limited to, static and/ordynamic random-access memory. Non-volatile memory may include, but isnot limited to, electrically erasable programmable read-only memory,phase change memory, resistive memory, and so forth.

The computing device 800 may further include input/output (I/O) devices808 (such as a display), keyboard, cursor control, remote control,gaming controller, image capture device, and so forth) and communicationinterfaces 810 (such as network interface cards, modems, infraredreceivers, radio receivers (e.g., Bluetooth), and so forth).

The communication interfaces 810 may include communication chips (notshown) that may be configured to operate the device 800 in accordancewith a Global System for Mobile Communication (GSM), General PacketRadio Service (GPRS), Universal Mobile Telecommunications System (UMTS),High Speed Packet Access (HSPA), Evolved HSPA (E-HSPA), or Long-TermEvolution (LTE) network. The communication chips may also be configuredto operate in accordance with Enhanced Data for GSM Evolution (EDGE),GSM EDGE Radio Access Network (GERAN), Universal Terrestrial RadioAccess Network (UTRAN), or Evolved UTRAN (E-UTRAN). The communicationchips may be configured to operate in accordance with Code DivisionMultiple Access (CDMA), Time Division Multiple Access (TDMA), DigitalEnhanced Cordless Telecommunications (DECT), Evolution-Data Optimized(EV-DO), derivatives thereof, as well as any other wireless protocolsthat are designated as 3G, 4G, 5G, and beyond. The communicationinterfaces 810 may operate in accordance with other wireless protocolsin other embodiments.

The above-described computing device 800 elements may be coupled to eachother via system bus 812, which may represent one or more buses. In thecase of multiple buses, they may be bridged by one or more bus bridges(not shown). Each of these elements may perform its conventionalfunctions known in the art. In particular, system memory 804 and massstorage devices 806 may be employed to store a working copy and apermanent copy of the programming instructions implementing theoperations described in reference to the sensor management module 150 ofFIG. 1. The various elements may be implemented by assemblerinstructions supported by processor(s) 802 or high-level languages thatmay be compiled into such instructions.

The permanent copy of the programming instructions may be placed intopermanent storage devices 806 in the factory, or in the field, through,for example, a distribution medium (not shown), such as a compact disc(CD), or through communication interface 810 (from a distribution server(not shown)). That is, one or more distribution media having animplementation of the agent program may be employed to distribute theagent and to program various computing devices.

The number, capability, and/or capacity of the elements 808, 810, 812may vary, depending on whether computing device 800 is used as astationary computing device, such as a set-top box or desktop computer,or a mobile computing device, such as a wearable device, tabletcomputing device, laptop computer, game console, or smartphone. Theirconstitutions are otherwise known, and accordingly will not be furtherdescribed.

At least one of processors 802 may be packaged together withcomputational logic 822 configured to practice aspects of embodimentsdescribed in reference to FIGS. 1-7. For one embodiment, at least one ofprocessors 802 may be packaged together with computational logic 822configured to practice aspects of sensor management and data processingto form a System in Package (SiP) or a System on Chip (SoC). For atleast one embodiment, the SoC may be utilized in, e.g., but not limitedto, a mobile computing device such as a computing wearable device,tablet, or smartphone. For example, computational logic 822 may beassociated with, or otherwise configured to include or access, sensormanagement module 150 and data processing module 146 similar to onesdescribed in reference to FIG. 1.

The computing device 800 may include or otherwise be associated with asensor module, such as sensor module 101 having sensors 106, 108, 110,for performing process data measurements according to the embodimentsdescribed above. In some embodiments, the sensor module 101 may becommunicatively coupled with the computing device 800 as described inreference to FIG. 1. For example, the sensor module 101 may be coupledwith, or otherwise be associated with, I/O devices 808.

In various implementations, the computing device 800 may comprise alaptop, a netbook, a notebook, an ultrabook, a smartphone, a tablet, apersonal digital assistant (PDA), an ultra mobile PC, a mobile phone, ora digital camera. In further implementations, the computing device 800may be any other electronic device that processes data.

The embodiments described herein may be further illustrated by thefollowing examples. Example 1 is an apparatus for predictive datameasurement, comprising: one or more sensors; and a sensor managementmodule coupled with the one or more sensors, wherein the sensormanagement module is to: initiate measurements of data indicative of aprocess, by the one or more sensors in a first data measurement mode;determine a pattern of events comprising the process, based on a portionof the measurements collected by the one or more sensors in the firstdata measurement mode over a time period, wherein the pattern is toindicate a prediction of appearance of events in the process; andinitiate measurements of the data by the one or more sensors in a seconddata measurement mode, wherein the second mode is based on the patternof events comprising the process.

Example 2 may include the subject matter of Example 1, and furtherspecifies that the sensor management module is to: determine, from thedata being measured in the second data measurement mode, that a match ofthe events to the pattern is above a margin of error; and revert to thefirst data measurement mode of data measurements in response to thedetermine that a match of the events to the pattern is above a margin oferror.

Example 3 may include the subject matter of Example 1, and furtherspecifies that the one or more sensors are selected from one or more of:an accelerometer, a gyroscope, a barometer, an infrared proximitysensor, a visible light sensor, microphone, compass, thermometer,moisture sensor, or a biometric sensor.

Example 4 may include the subject matter of Example 1, and furtherspecifies that the apparatus of claim 1, wherein to initiate the datameasurements in the first data measurement mode comprises to perform thedata measurements at a first sampling rate.

Example 5 may include the subject matter of Example 4, and furtherspecifies that to initiate the data measurements in the second datameasurement mode comprises to: perform the data measurements at a secondsampling rate that is lower than the first sampling rate; perform thedata measurements at a third sampling rate during first periods of timecorresponding to predicted appearances of events according to thedetermined pattern; or perform the data measurements at a fourthsampling rate during second periods of time between predictedappearances of consecutive events in the process and to perform the datameasurements at a fifth sampling rate during the first periods of time,wherein the fifth sampling rate is different than the fourth samplingrate.

Example 6 may include the subject matter of Example 5, and furtherspecifies that the fourth sampling rate is equal to or lower than thefirst sampling rate.

Example 7 may include the subject matter of Example 5, and furtherspecifies that to determine a pattern of events comprising the processincludes to identify data points and time instances approximately atwhich the identified data points are to be measured by the one or moresensors, wherein the measured data points match the identified datapoints within a desired margin of error.

Example 8 may include the subject matter of Example 7, and furtherspecifies that the time instances are within the first time period.

Example 9 may include the subject matter of Example 1, and furtherspecifies that the one or more sensors comprise multiple sensors,wherein the first data measurement mode comprises to perform the datameasurements at a first sampling rate by the multiple sensors, andwherein the second data measurement mode comprises: to perform the datameasurements at the first sampling rate by at least one of the multiplesensors and cease to perform the data measurements by at least anotherone of the multiple sensors, or to perform the data measurements at thefirst sampling rate by at least one of the multiple sensors and performthe data measurements at the second sampling rate by at least anotherone of the multiple sensors.

Example 10 may include the subject matter of Example 1, and furtherspecifies that the apparatus may further comprise a data processingmodule coupled with the one or more sensors to process the datameasurements, wherein the first and second data measurement modescomprise to perform the data measurements at a first sampling rate,wherein the data processing module is to process the data measurementsby the one or more sensors that are taken during periods of timecorresponding to the predicted appearance of events in the process.

Example 11 may include the subject matter of Examples 1 to 10, andfurther specifies that to initiate data measurements by the one or moresensors in a first data measurement mode includes to power on one ormore sensors.

Example 12 is a computer-implemented method for predictive datameasurement, comprising: initiating, by a computing device, measurementsof data indicative of a process by one or more sensors, in a first datameasurement mode; determining, by the computing device, a pattern ofevents comprising the process, based on a portion of the measurementscollected by the one or more sensors in the first data measurement modeover a time period, wherein the pattern indicates a prediction ofappearance of events in the process; and initiating, by the computingdevice, measurements of the data by the one or more sensors in a seconddata measurement mode, wherein the second mode is based on the patternof events comprising the process.

Example 13 may include the subject matter of Example 12, and furtherspecifies that the method may include determining, by the computingdevice, from the data measured in the second mode, that a match of theevents to the pattern is above a margin of error; and reverting, by thecomputing device, to the first data measurement mode of datameasurements in response to determining that a match of the events tothe pattern is above a margin of error.

Example 14 may include the subject matter of Example 12, and furtherspecifies that initiating measurements in a first data measurement modeincludes causing the one or more sensors to perform the datameasurements at a first sampling rate.

Example 15 may include the subject matter of Example 14, and furtherspecifies that initiating measurements of the data by the one or moresensors in a second data measurement mode includes one of: causing, bythe computing device, performance of the data measurements at a secondsampling rate that is lower than the first sampling rate; causing, bythe computing device, performance of the data measurements at a thirdsampling rate during first periods of time corresponding to predictedappearances of events according to the determined pattern; or causing,by the computing device, performance of the data measurements at afourth sampling rate during second periods of time between predictedappearances of consecutive events in the process, and performance of thedata measurements at a fifth sampling rate during the first periods oftime, wherein the fifth sampling rate is different than the fourthsampling rate.

Example 16 may include the subject matter of Examples 12 to 15, andfurther specifies that the events include appearance of data valueswithin a predicted data range that are measured by the one or moresensors approximately at a predicted time of appearance of the datavalues, according to the determined pattern.

Example 17 is a non-transient computing device-readable storage mediumhaving instructions for predictive data measurement that, in response toexecution on a computing device, cause the computing device to: initiatemeasurements of data indicative of a process by one or more sensorsaccessible by the computing device, in a first data measurement mode;determine a pattern of events comprising the process, based on a portionof the measurements collected by the one or more sensors in the firstdata measurement mode over a time period, wherein the pattern indicatesa prediction of appearance of events in the process; and initiatemeasurements of the data by the one or more sensors in a second datameasurement mode, wherein the second mode is based on the pattern ofevents comprising the process.

Example 18 may include the subject matter of Example 17, and furtherspecifies that the instructions, in response to execution on thecomputing device, further cause the computing device to: determine, fromthe data being measured in the second mode, that a match of the eventsto the pattern is above a margin of error; and revert to the first datameasurement mode of data measurements, in response to a determinationthat a match of the events to the pattern is above a margin of error.

Example 19 may include the subject matter of Examples 17 to 18, andfurther specifies that to initiate the data measurements in the firstdata measurement mode comprises to perform the data measurements at afirst sampling rate.

Example 20 may include the subject matter of Example 19, and furtherspecifies that to initiate the data measurements in the second datameasurement mode comprises to: perform the data measurements at a secondsampling rate that is lower than the first sampling rate; perform thedata measurements at a third sampling rate during first periods of timecorresponding to predicted appearances of events according to thedetermined pattern; or perform the data measurements at a fourthsampling rate during second periods of time between predictedappearances of consecutive events in the process and to perform the datameasurements at a fifth sampling rate during the first periods of time,wherein the fifth sampling rate is different than the fourth samplingrate.

Example 21 is an apparatus for predictive data measurement, comprising:means for initiating measurements of data indicative of a process by oneor more sensors accessible by the computing device, in a first datameasurement mode; means for determining a pattern of events comprisingthe process, based on a portion of the measurements collected by the oneor more sensors in the first data measurement mode over a time period,wherein the pattern indicates a prediction of appearance of events inthe process; and means for initiating measurements of the data by theone or more sensors in a second data measurement mode, wherein thesecond mode is based on the pattern of events comprising the process.

Example 22 may include the subject matter of Example 21, and furtherspecifies that means for determining, from the data being measured inthe second mode, that a match of the events to the pattern is above amargin of error; and means for reverting to the first data measurementmode of data measurements, in response to a determination that a matchof the events to the pattern is above a margin of error.

Example 23 may include the subject matter of Examples 21 to 22, andfurther specifies that the means for initiating measurements in a firstdata measurement mode comprise means for performing the datameasurements at a first sampling rate.

Example 24 may include the subject matter of Example 23, and furtherspecifies that the means for initiating the data measurements in thesecond data measurement mode comprises: means for performing the datameasurements at a second sampling rate that is lower than the firstsampling rate; means for performing the data measurements at a thirdsampling rate during first periods of time corresponding to predictedappearances of events according to the determined pattern; or means forperforming the data measurements at a fourth sampling rate during secondperiods of time between predicted appearances of consecutive events inthe process and performing the data measurements at a fifth samplingrate during the first periods of time, wherein the fifth sampling rateis different than the fourth sampling rate.

Various operations are described as multiple discrete operations inturn, in a manner that is most helpful in understanding the claimedsubject matter. However, the order of description should not beconstrued as to imply that these operations are necessarily orderdependent. Embodiments of the present disclosure may be implemented intoa system using any suitable hardware and/or software to configure asdesired.

Although certain embodiments have been illustrated and described hereinfor purposes of description, a wide variety of alternate and/orequivalent embodiments or implementations calculated to achieve the samepurposes may be substituted for the embodiments shown and describedwithout departing from the scope of the present disclosure. Thisapplication is intended to cover any adaptations or variations of theembodiments discussed herein. Therefore, it is manifestly intended thatembodiments described herein be limited only by the claims and theequivalents thereof.

What is claimed is:
 1. An apparatus for collecting measurements of aprocess, comprising: a sensor circuitry including one or more sensors,to collect measurements of the process; a power source coupled with thesensor circuitry to supply power to the one or more sensors; and one ormore sensor management and data processing modules coupled with thesensor circuitry, to regulate the power source in supplying power to theone or more sensors and operation of the one or more sensors, whichincludes to: operate the power source to power on the one or moresensors, to measure the process at a first sampling rate, in a firstdata measurement mode, over a time period; determine a pattern of eventsmaking up the process, based on the measurements collected by the one ormore sensors in the first data measurement mode over the time period,wherein to determine the pattern of events includes to identify datapoints of the process and time intervals during which the data pointsare to be measured by the one or more sensors, wherein the patternincludes appearance of the identified data points in the identified timeintervals; operate the power source to power on the one or more sensorsto measure the process in a second data measurement mode, which includesto measure the data points at a second sampling rate during theidentified time intervals, and refrain from the measurements at thefirst sampling rate between the identified time intervals, wherein thesecond sampling rate is different than the first sampling rate.
 2. Theapparatus of claim 1, wherein the one or more sensors are selected from:an accelerometer, a gyroscope, a barometer, an infrared proximitysensor, a visible light sensor, a microphone, a compass, a thermometer,a moisture sensor, or a biometric sensor.
 3. The apparatus of claim 1,wherein the second sampling rate is lower than the first sampling rate.4. The apparatus of claim 1, wherein the measured data points match theidentified data points within a margin of error.
 5. The apparatus ofclaim 1, wherein the one or more sensor management and data processingmodules are to determine, from the data measured in the second datameasurement mode, that a match of the measured data points to theidentified data points is above a margin of error; and revert to thefirst data measurement mode of data measurements.
 6. The apparatus ofclaim 1, wherein the one or more sensors comprise multiple sensors,wherein to measure the process in the first data measurement modecomprises to perform the measurements at the first sampling rate by themultiple sensors, and wherein the second data measurement modecomprises: to perform the measurements by at least one of the multiplesensors and cease to perform the measurements by at least another one ofthe multiple sensors, or to perform the measurements at the secondsampling rate by at least one of the multiple sensors and perform themeasurements at a third sampling rate by at least another one of themultiple sensors.
 7. The apparatus of claim 1, wherein the one or moresensor management and data processing modules is to process themeasurements of the process.
 8. A computer-implemented method,comprising: operating, by a computing device of a system, a power sourcecoupled with a sensor circuitry of the system, to power on one or moresensors of the system, to cause the one or more sensors to performmeasurements of a process in a first data measurement mode, at a firstsampling rate, over a first time period; determining, by the computingdevice, a pattern of events making up the process, based on themeasurements collected by the one or more sensors in the first datameasurement mode over the first time period, including identifying datapoints of the process and time intervals during which the data pointsare to be measured by the one or more sensors, wherein the patternincludes appearance of the identified data points in the identified timeinterval; and operating, by the computing device, the power source topower on the one or more sensors to measure the process in a second datameasurement mode, including measuring the data points at a secondsampling rate during the identified time intervals, and refraining fromthe measurements at the first sampling rate between the identified timeintervals, wherein the second sampling rate is different than the firstsampling rate.
 9. The computer-implemented method of claim 8, furthercomprising: determining, by the computing device, from the data measuredin the second data measurement mode, that a match of the measured datapoints to the identified data points is above a margin of error; andreverting, by the computing device, to the measurements in the firstdata measurement mode.
 10. The computer-implemented method of claim 8,wherein the second sampling rate is lower than the first sampling rate.11. A non-transient computing device-readable storage medium havinginstructions that, in response to execution on a computing device of asystem, cause the computing device to: operate a power source coupledwith a sensor circuitry of the system, to power on one or more sensorsof the system, to cause the one or more sensors to perform measurementsof a process, in a first data measurement mode, which includes aperiodic performance of the measurements of the process at a firstsampling rate, over a time period; determine a pattern of events makingup the process, based on the measurements collected by the one or moresensors in the first data measurement mode over the time period, whereinto determine the pattern of events includes to identify data points ofthe process and time intervals during which the data points are to bemeasured by the one or more sensors, wherein the pattern includesappearance of the identified data points in the identified timeintervals; and operate the power source to power on the one or moresensors to measure the process in a second data measurement mode, whichincludes to measure data points at a second sampling rate during theidentified time intervals, and refrain from performance of themeasurements at the first sampling rate between the identified timeintervals, wherein the second sampling rate is different than the firstsampling rate.
 12. The non-transient computing device-readable storagemedium of claim 11, wherein the instructions, in response to executionon the computing device, further cause the computing device to:determine, from the data being measured in the second data measurementmode, that a match of the events to the pattern is above a margin oferror; and revert to the first data measurement mode of datameasurements.
 13. The non-transient computing device-readable storagemedium of claim 11, wherein the second sampling rate is lower than thefirst sampling rate.